Data-driven disaster management in a smart city

Disasters, both natural and man-made, are complex events that result in the loss of human life and/or the destruction of properties. The advances in Information Technology (IT) and Big Data Analysis represent an opportunity for the development of resilient environments, since from the application of Big Data (BD) technologies it is possible not only to extract patterns of occurrences of events, but also to predict them. The work carried out in this dissertation aims to apply the CRISP-DM methodology to conduct a descriptive and predictive analysis of the events that occurred in the city of Lisbon, with emphasis on the events that affected buildings. Through this research it was verified the existence of temporal and spatial patterns of occurrences with some events occurring in certain periods of the year, such as floods and collapses that are recorded more frequently in periods of high precipitation. The spatial analysis showed that the city center is the area most affected by the occurrences, and it is in these areas where the largest proportion of buildings with major repair needs are concentrated. Finally, machine learning models were applied to the data, and the Random Forest model obtained the best result with an accuracy of 58%. This research contributes to improve the resilience of the city since the analysis developed allowed to extract insights regarding the events and their occurrence patterns that will help the decision-making process.Os desastres, tanto naturais quanto as provocadas pelo homem, são eventos complexos que se traduzem em perdas de vidas e/ou destruição de propriedades. Os avanços na área de Tecnologias de Informação e Big Data Analysis representam uma oportunidade para o desenvolvimento de ambientes resilientes dado que, a partir da aplicação das tecnologias de Big Data (BD), é possível não só extrair padrões de ocorrências dos eventos, mas também fazer a previsão dos mesmos. O trabalho realizado nesta dissertação visa aplicar a metodologia CRISP-DM de forma a conduzir análises descritivas e preditivas sobre os eventos que ocorreram na cidade de Lisboa, com ênfase nos eventos que afetaram os edifícios. A investigação permitiu verificar a existência de padrões temporais e espaciais eventos a ocorrer em certos períodos do ano, como é o caso das cheias e inundações que são registados com maior frequência nos períodos de alta precipitação. A análise espacial permitiu verificar que a área do centro da cidade é a área mais afetada pelas ocorrências sendo nestas áreas onde se concentram a maior proporção de edifícios com grandes necessidades de reparação. Por fim, modelos de aprendizagem automática foram aplicados aos dados tendo o modelo Random Forest obtido o melhor resultado com accuracy de 58%. Esta pesquisa contribui para melhorar o aumento da resiliência da cidade pois, a análise desenvolvida permitiu extrair insights sobre os eventos e os seus padrões de ocorrência que irá ajudar os processos de tomada de decisão

Enablers in Crisis Information Management: A Literature Review

Social media often plays a central role in crisis informatics as it is an important source for assessing, understanding, and locating crises quickly and accurately. In addition, social media enables actors to react more effectively and efficiently when managing crises. However, enablers of crisis information management have not been carved out explicitly in a systematic view. Therefore, we perform a literature review to synthesize the existing literature on crisis information management with a focus on technical enablers and their classification into the crisis-management phases. As our results show, searching for crisis informatics mostly results in social media-related publications. We found that Twitter is one of the most important technical enablers but that research on other social media platforms is underrepresented. Also, most publications center on the post-crisis phases of crisis management, leaving out the pre-crisis phases

National Inquiry on Bushfire Mitigation and Management

Bushfires are an inherent part of the Australian environment. We cannot prevent them, but we can minimise the risks they pose to life, property and infrastructure, production systems, and the environment. Australia has a large and very capable force of volunteer and career firefighters, advanced firefighting technologies, and significant firefighting resources. But the geographical scale of our country, the large and expanding rural–urban interface, and the potential for rapid bushfire development and spread under adverse weather conditions mean that individual Australians cannot rely solely on fire agencies to protect their lives and property from bushfires. Bushfires have a fundamental and irreplaceable role in sustaining many of Australia’s natural ecosystems and ecological processes and are a valuable tool for achieving land management objectives. However, if they are too frequent or too infrequent, too severe or too mild, or mistimed, they can erode ecosystem health and biodiversity and compromise other land management goals. We have been learning to live with fire since the first Australians arrived on our continent. We need to continue, and enrich, that learning process in contemporary circumstances and be able to adapt our planning and responses to change. This report seeks to help all Australians meet these challenges

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Department of Computer Science and EngineeringA large-scale disaster such as earthquakes and tsunami can cause billion-dollar destruction to a city and kill many people. To mitigate the dead troll, fast disaster response to rescue survivors in a disaster zone is of paramount importance. However, it is difficult to find the location of the injured people in a disaster zone due to the debris and smoke in collapsed buildings as well as the disruption of communication networks. This can cause poor decisions of the disaster response team about where to deploy the rescue personnel and allocate the resource. Therefore, we propose to develop an AI system to predict the location of injured people in a disaster area. In this research, our system has three major parts: (1) the prediction of the density of injured people in a gridand (2) the strategy of the rescue team to search for injured peopleand (3) the deployment the rescue team to search the location of the most density injured people area according to the first and second part. In the first part, we developed a deep learning software package that consists of state-of-the-art deep learning techniques such as attention module and data annotation to predict the density of injured civilians. Our work uses a disaster simulator called RoboCup Rescue Simulation (RCRS). To predict the density of injured people in RCRS, we train the machine learning model using the two cases of the image data: (1) single image frame such as a satellite imageand (2) multiple image sequence frame such as disaster video clip. Furthermore, we evaluate our ML model in the other two domains: (1) the prediction of the location of crime in Chicagoand (2) the prediction of the location of RSNA Pneumonia. In the second part, we propose the Treasure Hunt Problem. In RCRS, the rescue team has to search more than one injured people and it is a complicated multi-agent problem. Therefore, study a simpler problem called the Treasure Hunt Problem, in which there is only one rescue crew search the only one injured civilian. In this problem, we assume that the location of the treasure is determined based on the probability distribution, and the ML model predicts the distribution of probability that treasure exists for each coordinate within the map. To solve this problem, we propose two search strategies that makes use of the ML model to improve the effectiveness of a search mission: (1) the probabilistic greedy search that the hunter searches preferentially for the cell with the highest probability of existing treasure given by ML modeland (2) the probabilistically admissible heuristic A* search that the hunter searches the cell determined by heuristic A* search with the probability of existing treasure given by ML model. In the last part, we merge the first and second parts to search for the location of the most density injured people area. To predict the location, we predict the number of injured people with several ML models used in the first part and we convert the injured people density predicted to the probability distribution. And the rescue team search the most density injured people area according to the search strategy of the second part based on this probability distributionclos

A Human Factors analysis of Firefighter injury sustained during emergency response operations:Implications for error management and injury reduction in English Fire and Rescue Services.

This research is concerned with the human factors that may contribute to firefighter injury and whether the Fire and Rescue Service (FRS) adequately acknowledges their influence when investigating, recording, analysing, or reporting accident causation. In particular, the extent to which, as critical decision makers, firefighters experience the deficit outcome of their own risk-v-benefit decisions when operating without the immediate oversight of a supervisor or commander. Studies of judgement and decision making specifically focused on the role of firefighter as opposed to their incident commanders are exceptional.For the first time in the analysis of firefighter injury, a number of variables that represent the preconditions of accident causation such as the demographic, temporal, environmental and contextual characteristics were analysed. An ‘error typing’ taxonomy that differentiates between decision errors, skill-based errors, perception errors and violations was used to examine the extent to which human factors are being considered by FRSs in the analysis of firefighter injury. Opportunity was also taken to examine the applicability of the Human Factors Analysis and Classification System (HFACS) (Weigmann and Shappell 2003), to the emergency response domain of the FRS. This revealed the value of developing a valid and reliable sector specific variant of HFACS (UKFire-HFACS). Finally, using the critical decision method, recollection of the contextual characteristics that influenced the judgements, decisions, and actions at the ‘moment-of-choice’ of injured firefighters was also explored. Three studies that when combined establish components of a Human Factors Analysis Framework (HFAF) for the FRS.It was established that when implementing the requirements of an incident commander’s tactical plan, firefighters are required to make critical decisions and at times experience injury when operating without the immediate oversight of a supervisor or commander. Analysis demonstrated how the majority of injuries involve either a decision based or skill-based error which substantiates the existence and influence of skill fade at the ‘moment-of-choice’. It also brings FRS arrangements for the maintenance of competence into focus and worthy of closer scientific scrutiny. It is also evident that the approach of this research using three studies can be developed into a human factors analysis framework for the FRS. In turn this can establish the means by which the deficit outcome of firefighter critical decision making can be better understood, enable targeted intervention, and over time, reduce reported operational injury

Factors Influencing Fire Safety in Brazil

Research was conducted on factors influencing fire safety in Brazil and how implementation of a national fire incident reporting system could help. Review of fire incident reporting systems from the United States, United Kingdom, Finland, Singapore, and Hong Kong revealed several attributes, which if implemented, could help improve the fire problem in Brazil. However, research suggests that several other factors need to be addressed first, including the challenges associated with favelas, fire safety culture, fire regulations and fire service resources

A Semantics-Based Common Operational Command System for Multiagency Disaster Response

Disaster response is a highly collaborative and critical process that requires the involvement of multiple emergency responders (ERs), ideally working together under a unified command, to enable a rapid and effective operational response. Following the 9/11 and 11/13 terrorist attacks and the devastation of hurricanes Katrina and Rita, it is apparent that inadequate communication and a lack of interoperability among the ERs engaged on-site can adversely affect disaster response efforts. Within this context, we present a scenario-based terrorism case study to highlight the challenges of operational disaster command and response. In this article, which is based on the French emergency response doctrine, we outline a semantics-based common operational command system that is designed to guarantee an efficient information flow among ERs. Our focus is on offering to all ERs, a real-time operational picture of the situation in order to enable multilevel coordination among firefighters, police, healthcare units, public authorities, and other stakeholders. Our approach consolidates information to promote timely sharing of data among ERs. The proposed system is based on an ontology that has been developed to represent the different types of knowledge on the part of ERs, providing a shared vocabulary that covers a variety of interoperability concerns

Indoor And Outdoor Real Time Information Collection in Disaster Scenario

A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed